Scope

Bayesian networks (also known as causal probabilistic networks)
with their associated methods have now been around in medicine for
more than a decade. They have become increasingly popular for
representing and handling uncertain knowledge in
medicine. Almost simultaneously, the use of Bayesian
statistics has increased in popularity in medicine. Currently,
interest is emerging within bioinformatics to use Bayesian
methods for building models of various kind. This workshop
aims to bring together researchers in these fields in order to assess
the current state of the art, to identify obstacles for progress and
to determine future research directions. The workshop's aim is also to
promote research collaboration among different groups in these fields.

Bayesian models are used in medicine to assist in the diagnosis of
disorders and to predict the natural course of disease or outcome
after treatment (prognosis). They are also being used as part
of models to determine the optimal treatments of a disorder in
patients, or to predict outcome in groups of patients. The
Bayesian approach has the advantage that evidence can be easily
incorporated into statistical models. Another advantage is that
evidence can be handled readily when using a Bayesian model to solve
an actual medical problem. Furthermore, Bayesian models cannot only
be developed by extracting probabilistic information from datasets;
graphical models like Bayesian networks can also be constructed with
the help of medical domain experts or by consulting relevant
biomedical literature. Typically, Bayesian networks rely for their
construction on causal, in particular (patho)physiological models of
disease. Bayesian networks have also been used successfully for the
construction of dynamic, temporal statistical models. The fact
that Bayesian models allow for the easy incorporation of knowledge of
background populations, explains that they are also increasingly used
in research on risk models of disease, associating risk with
spatial distribution of populations. When used for the
prediction of life expectancy, they can be combined with traditional
models of life expectancy. In the context of medical decision
making, Bayesian models can be easily integrated with decision
theory to yield models for the selection of optimal treatments, or to
develop models for healthcare planning under uncertainty.

Topics

It is expected that papers will explicitly discuss one or more of the
topics mentioned below in the context of medicine.

Probability assessment in the context of the construction of
Bayesian models from data

Refinement of Bayesian models using data

Handling missing values in data

Evaluation

Methodologies for the evaluation of Bayesian models

Evaluation of Bayesian networks for concrete medical domains

Submissions have been refereed by at least two and in most cases three
members of the programme
committee. Accepted papers will appear in the working notes of the
workshop "Bayesian Models in Medicine". The authors of the
best papers of the workshop will be invited to submit an extended
version of their paper to a special issue of the journal
Artificial Intelligence in Medicine on Bayesian Models in Medicine.

Instructions to authors

The papers (up to 5 pages) are to be sent as a compressed Postscript file by
e-mail before 10 June 2001 to all three co-chairs and should be written in
English with a brief abstract. Formatting instructions are as follows:
the abstract should be formatted in two-column format, with Times
Roman type face, pointsize 10, with title and names of the authors in
bold font. Left and right margins should be 2 cm, text height 23 cm,
and text width 16.9 cm; the two columns should be separated by 0.5 cm
white space. A sample paper is available.